YoloV5-API

API to run inferences with YoloV5 models. Written in C++, based on OpenCV 4.5.5

Setup

Data directory must contain your config.json

config.json defines:

• ONNX absolute model path
• input size (640 default)
• array of class names

A dummy example is available in the data/ folder

Docker

docker pull mascit/yolov5-api

To run the container, you first need to mount your data folder containing config.json and your onnx model.

docker run --name yolov5-api -v <path to data on host>:/app/data -p <port>:5000 mascit/yolov5-api

Remember to use a container-relative path for your model_path field in config.json

Build

Or, just build it from source.

cmake --configure .
cmake --build . --target main -j <num jobs>

Endpoints

/predict [POST]

Body

• Image bytes (binary in Postman)

Headers

• X-Confidence-Thresh
  • default 0.5
• X-NMS-Thresh
  • default 0.45
• X-Return
  • image_with_boxes
    • A JPEG image with drawn predictions
  • json (default)
    • A json array containing predictions. Each object defines: xmin, ymin, xmax, ymax, conf, class_name